GANs for Protein Structure Prediction

  • Juan Andrés Morales Cordovilla (Organiser)

Activity: Participating in or organising an eventOrganising a conference, workshop, ...

Description

The protein structure prediction from its query amino acid sequence has been considered for many year a very hard Bioinformatics problem. Recently [1] there has been a breakthrough on this area thanks to the incorporation of "Direct Coupling Analysis" (DCA, based on coevolution) that allows to predict long-distance contacts and consequently to estimate a Contact Map (CMap) image. This image can be considered noisy and recently we have enhanced it using Deep Convolutional Neural Networks. In this talk we will discuss the possibility of using GANs to enhance this image.
Period23 Jul 2018
Event typeGuest talk
LocationAustriaShow on map

Fields of science

  • 305 Other Human Medicine, Health Sciences
  • 102019 Machine learning
  • 304 Medical Biotechnology
  • 303 Health Sciences
  • 302 Clinical Medicine
  • 301 Medical-Theoretical Sciences, Pharmacy
  • 102 Computer Sciences
  • 106005 Bioinformatics
  • 106007 Biostatistics
  • 304003 Genetic engineering
  • 106041 Structural biology
  • 102010 Database systems
  • 101018 Statistics
  • 106023 Molecular biology
  • 106002 Biochemistry
  • 102001 Artificial intelligence
  • 102015 Information systems
  • 101004 Biomathematics
  • 102004 Bioinformatics

JKU Focus areas

  • Health System Research
  • Computation in Informatics and Mathematics
  • Clinical Research on Aging
  • Nano-, Bio- and Polymer-Systems: From Structure to Function
  • Medical Sciences (in general)